Senior MLOps Engineer

Versatile People; Deutschland; Remote; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Versatile People is seeking a seasoned ML Platform Engineer to design and implement scalable MLOps infrastructure in a 100% remote role. The candidate will bridge data science and operations, enabling seamless development, training, deployment and maintenance of ML models.

The ideal candidate has 10+ years in ML platforms, strong cloud experience (AWS/GCP/Azure), and expertise in DevOps, Python, Docker and Kubernetes. Remote work with overlapping PST hours is required.

Qualifikationen

• Bachelor’s or Master’s degree in Engineering/CS or equivalent.
• 10+ years of professional experience building cloud-based applications, including ML platforms.
• Strong expertise in ML workflows, model training and deployment processes.
• Deep knowledge of cloud platforms (AWS/GCP/Azure) and DevOps practices (CI/CD, containers).
• Advanced Python programming for ML applications.
• Experience designing secure, scalable systems compliant with regulations.

Aufgaben

• Evaluate and select cloud services for each stage of the ML lifecycle.
• Design and implement the architecture of the MLOps platform.
• Set up automated pipelines for data prep, model training, and deployment.
• Implement version control for code, data, and models for reproducibility.
• Ensure the platform is scalable, secure, and compliant with regs.
• Provide tools and interfaces for data scientists to utilize the platform.
• Continuously optimize platform performance and cost-efficiency.

Kenntnisse

Machine Learning

DevOps

Python

Ausbildung

Bachelor’s/Master’s degree in Engineering or Computer Science

Tools

Docker

Kubernetes

Infrastructure as Code

Jobbeschreibung

IMPORTANT

Geography: Candidates based in India
Job type: Permanent
Work type: 100% Remote
Notice Period: 2 weeks or less
Engagement: Full-time, 4-5 hours (PST overlap)

ABOUT THE ROLE

A leading company specializing in delivering cutting-edge AI and machine learning solutions is seeking a seasoned ML Platform Engineer. This role involves designing and implementing scalable MLOps platforms for Fortune 500 clients, enabling seamless development, deployment, and maintenance of machine learning models. The successful candidate will leverage deep expertise in cloud platforms and DevOps practices to bridge the gap between data science and operations, contributing to transformative AI projects.

Responsibilities:

• Evaluate and select appropriate cloud services for each stage of the ML lifecycle.
• Design and implement the overall architecture of the MLOps platform.
• Set up automated pipelines for data preparation, model training, and deployment.
• Implement version control for code, data, and models to maintain reproducibility and traceability.
• Ensure the platform is scalable, secure, and compliant with relevant regulations.
• Provide tools and interfaces for data scientists to easily leverage the platform.
• Continuously optimize the platform for performance and cost-efficiency.
• Develop tools and interfaces for data scientists to easily utilize the platform's capabilities.
• Bridge the gap between data science and operations, enabling efficient development, deployment, and maintenance of ML models at scale.

Must Haves:

• Bachelor’s/Master’s degree in Engineering, Computer Science, or equivalent experience.
• At least 10+ years of professional experience in building cloud-based applications, including ML platforms.
• Mandatory Skills: Machine Learning: 10 years, DevOps: 10 years
• Extensive expertise in cloud platforms like AWS, Google Cloud Platform, or Azure (Azure preferred).
• Proficiency in DevOps practices, including CI/CD pipelines, containerization (Docker, Kubernetes), and infrastructure as code.
• Strong understanding of ML workflows, model training, deployment processes, and data engineering pipelines.
• Advanced programming skills, particularly in Python, with a focus on ML applications.
• Experience in designing secure, scalable systems that adhere to regulatory compliance.
• Demonstrated ability to collaborate with cross-functional teams, including data scientists and software engineers.